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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

ARM Radar Science Recommendations from 2017 6th ARM Radar Workshop

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s radar organization was formed to provide an integrated approach to meet the scientific objectives of ARM. The radar organization consists of two groups: Radar Science and Radar Engineering and Operations. The two groups work closely for planning, coordination, and communications to produce high-quality observations and products from the ARM radar network, which consists of complex radars deployed in very challenging environments. In addition, scanning cloud radars are relatively new in the community, with very limited products that are mature enough to be applied for modeling studies. The Radar Science Group and Radar Engineering and Operations Group meet regularly along with researchers and users from the community in a workshop. The overarching objective of the workshop is to maximize the usage of ARM radar data for improving our understanding of processes and, ultimately improving representation of clouds and precipitation in models. The 6th DOE ARM Radar Engineering and Radar Science Workshop was held October 24-26, 2017, at Stony Brook University. Approximately 40 participants were present; a detailed list of participants is listed in Section 5. This report documents recommendations made by members of the science community who attended the workshop. Their recommendations appear italicized in the report. ARM management will consider these recommendations in their future planning and prioritization activities. The topics addressed during the workshop are contained in the agenda (Section 4). Significant action items, recommendations, and comments that came out of the meeting are contained in Section 3. The content in Section 3 presents a path through important discussions that were pursued during the Workshop.

Southern Great Plains, North Slope of Alaska, East↗
DOE OSTI2016

Watchdog for ARM Radar Network Operations

WARNO is a software system designed to monitor the radars in the ARM Radar Network. It allows real time monitoring and tracking of instrument state and condition. It provides a web portal on the front end to interact with users, a REST API webpoint for interactions with third party systems, and an internal distributed architecture to allow it to be deployed at multiple sites.

Source record↗
DOE OSTI2013

Python-ARM Radar Toolkit

The Python-ARM Radar Toolkit (Py-ART) is a collection of radar quality control and retrieval codes which all work on two unifying Python objects: the PyRadar and PyGrid objects. By building ingests to several popular radar formats and then abstracting the interface Py-ART greatly simplifies data processing over several other available utilities. In addition Py-ART makes use of Numpy arrays as its primary storage mechanism enabling use of existing and extensive community software tools.

Jonathan Helmus, ScottCollis↗
DOE OSTI2013

Python-ARM Radar Toolkit

The Python-ARM Radar Toolkit (Py-ART) is a collection of radar quality control and retrieval codes which all work on two unifying Python objects: the PyRadar and PyGrid objects. By building ingests to several popular radar formats and then abstracting the interface Py-ART greatly simplifies data processing over several other available utilities. In addition Py-ART makes use of Numpy arrays as its primary storage mechanism enabling use of existing and extensive community software tools.

Jonathan Helmus, Scott Collis↗
DOE OSTI2016

The Python ARM Radar Toolkit (Py-ART), a library for working with weather radar data in the Python programming language

The Python ARM Radar Toolkit is a package for reading, visualizing, correcting and analysing data from weather radars. Development began to meet the needs of the Atmospheric Radiation Measurement Climate Research Facility and has since expanded to provide a general-purpose framework for working with data from weather radars in the Python programming language. The toolkit is built on top of libraries in the Scientific Python ecosystem including NumPy, SciPy, and matplotlib, and makes use of Cython for interfacing with existing radar libraries written in C and to speed up computationally demanding algorithms. As a result, the source code for the toolkit is available on GitHub and is distributed under a BSD license.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

FY 2018 ARM Radar Plan

The fundamental objective of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility radar facility is to provide high-quality radar observations to the scientific user community with an overarching goal of improving the treatment of clouds and precipitation in climate models. Historically, ARM has operated zenith-profiling cloud radars at millimeter wavelengths at five of its facilities. Since 2010, 16 scanning systems have been added to the radar facility. The ARM radar facility has 33 radars in its possession at three fixed sites and three mobile facilities. A breakdown of the ARM radar assets is given below: 8 Scanning ARM Clouds Radar Systems (SACR: two radars on single pedestal; six 1st- and two 2nd- generation) 7 Ka-band ARM Zenith profiling Radar (KAZR: five 1st- and two 2nd-generation) 5 X-band Scanning ARM precipitation Radar (XSAPR: four 1st- and one 2nd-generation) 3 C-band Scanning ARM precipitation Radar (CSAPR: two 1st- and one 2nd-generation) 1 W-band ARM Cloud Radar (WACR) 1 Marine W-band ARM Cloud Radar (MWACR: configured to be deployed on a stable table or scanning pedestal). Of these radars, all but six (two SACRs [accounting for four radars], one KAZR, and one 1st-generation CSAPR) have operational assignments. Therefore, the current measurement strategy involves operating 27 radars, not including radar wind profilers (RWPs). This array of instruments represents a combination of scanning and profiling radars at ARM’s sites. In addition to the scanning capabilities, the radars at each site differ in operating frequency, technology, and complexity, but all are complex systems. Some of them were custom designed and built specifically for ARM. Typically, newer systems take a few years to reach operational maturity with adequate characterization, calibration, testing, and quality control. Due to various factors, not all of the radars have achieved an operationally mature status sufficient to provide high-quality data in a research environment. In addition, these radars are often deployed in remote locations and harsh environments to meet science needs. The remoteness of such complex systems is a considerable challenge for continuous operations. Tools needed for a research-level operational radar are not readily available from vendors, and many of the necessary system management tools, both hardware and software, to manage these systems must be developed in-house for such deployments. Based on experience over the past seven years, including conclusions from the first phase of the radar plan, implemented in mid-2016, it is clear that ARM cannot support the operation of all 27 currently deployed radars and expect to provide research-quality data (including higher-order products) with the current resources. Experience from the first phase of the radar plan has provided a better indication than has been available previously of what size radar network is reasonable. A preliminary proposal for what that radar network could look like is provided here. The plan presented here uses the available resources to best support this proposed network.

ARM Mobile Facility↗
DOE OSTI2017

Python ARM Radar Toolkit (Py-ART) Roadmap

Radar software is key for producing scientific insight from radar data. There are several platforms for interacting with radar data; the open-source variants are well documented in Heistermann et al. 2014. The Python ARM Radar Toolkit (Py-ART; Helmus and Collis, 2016) is one of these. This document is a guiding roadmap for development for Py-ART. It sets priorities for U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility-funded work and it advises the community which contributions are desired and will receive the most assistance from ARM-funded software engineers.

Python ARM Radar Toolkit, linear programming, valu↗
DOE OSTI2019

The ARM Radar Network: At the Leading-edge of Cloud and Precipitation Observations

The ARM observatories include radars that span a range of frequencies and capabilities to characterize the three-dimensional structure and properties of atmospheric hydrometeors. Improving our ability to predict future weather and climate conditions is strongly linked to achieving significant advancements in our understanding of cloud and precipitation processes. Observations are critical to making these advancements because they both improve our understanding of these processes and provide constraints on numerical models. Historically, instruments for observing cloud properties have limited cloud-aerosol investigations to a small subset of cloud-process interactions. To address these challenges, the last decade has seen the U.S. DOE ARM facility significantly upgrade and expand its surveillance radar capabilities towards providing holistic and multi-scale observations of clouds and precipitation. These upgrades include radars that operate at four frequency bands covering a wide range of scattering regimes, improving upon the information contained in earlier ARM observations. The traditional ARM emphasis on the vertical column is maintained, providing more comprehensive, calibrated, and multiparametric measurements of clouds and precipitation. Furthermore, the ARM radar network now features multiple scanning dual-polarization Doppler radars to exploit polarimetric and multi-Doppler capabilities that provide a wealth of information on storm microphysics and dynamics under a wide range of conditions. While the diversity in wavelengths and detection capabilities are unprecedented, there is still considerable work ahead before the full potential of these radar advancements is realized. This includes synergy with other observations, improved forward and inverse modeling methods, and well-designed data-model integration methods. The overarching goal is to offer a comprehensive characterization of a complete volume of the cloudy atmosphere and to act as a natural laboratory for the study of cloud processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Advanced Precipitation and Boundary Layer Data Products Derived from ARM Radar Wind Profilers

This research project was successful in delivering on four main objectives. First, software was developed to accurately calculate 915-MHz radar wind profiler (RWP) spectrum moments from the recorded Doppler velocity power spectra. Second, software was developed to calibrate the RWP reflectivity factor using collocated surface disdrometer observations. Third, the Python processing code was documented and given to the ARM Infrastructure to produce ARM ‘b level’ calibrated RWP products. Fourth, calibrated RWP products were uploaded to the ARM Archive as PI Products for 10 years of SGP RWP observations and for GoAmazon and TRACER field campaign RWP observations. In addition to working with RWP observations, this research project also worked with KAZR observations to distinguish insects from boundary layer clouds to help improve the ARSCL cloud mask product. The PI worked with senior and early career ARM funded scientists at BNL exploring how to include calibrated RWP moments into future versions of the ARSCL product.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Scanning ARM Cloud Radars. Part II: Data Quality Control and Processing

The Scanning ARM Cloud Radars (SACR’s) are the primary instruments for documenting the four-dimensional structure and evolution of clouds within a 20-30 km radius from the ARM fixed and mobile sites. Here, the post-processing of the calibrated SACR measurements is discussed. First, a feature mask algorithm that objectively determines the presence of significant radar returns is described. The feature mask algorithm is based on the statistical properties of radar receiver noise. It accounts for atmospheric emission and is applicable even for SACR profiles with few or no signal-free range gates. Using the nearest-in-time atmospheric sounding, the SACR radar reflectivities are corrected for gaseous attenuation (water vapor and oxygen) using a line-by-line absorption model. Despite having a high pulse repetition frequency, the SACR has a narrow Nyquist velocity limit and thus Doppler velocity folding is commonly observed. An unfolding algorithm that makes use of a first guess for the true Doppler velocity using horizontal wind measurements from the nearest sounding is described. The retrieval of the horizontal wind profile from the HS-RHI SACR scan observations and/or nearest sounding is described. The retrieved horizontal wind profile can be used to adaptively configure SACR scan strategies that depend on wind direction. Several remaining challenges are discussed, including the removal of insect and second-trip echoes. The described algorithms significantly enhance SACR data quality and constitute an important step towards the utilization of SACR measurements for cloud research.

climate↗
DOE OSTI2014

Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

This study presents radar-based precipitation estimates collected during the two-month DOE ARM - NASA Midlatitude Continental Convective Clouds Experiment (MC3E). Emphasis is on the usefulness of radar observations from the C-band and X-band scanning ARM precipitation radars (CSAPR, XSAPR) for rainfall estimation products to distances within 100 km of the Oklahoma SGP facility. A dense collection of collocated ARM, NASA GPM and nearby surface Oklahoma Mesonet gauge records are consulted to evaluate potential ARM radar-based hourly rainfall products and campaign optimized methods over individual gauge and areal characterizations. Rainfall products are evaluated against the performance of the regional operational NWS NEXRAD S-band radar polarimetric product. Results indicate that the ARM C-band system may achieve similar point and areal-gauge bias and root mean square (rms) error performance to the NEXRAD standard for the variety of MC3E deep convective events sampled when capitalizing on differential phase measurements. The best campaign rainfall performance was achieved when applying radar relations capitalizing on estimates of the specific attenuation from the CSAPR system. The ARM X-band systems only demonstrate solid capabilities as compared to NEXRAD standards for hourly point and areal rainfall accumulations under 10 mm. Here, all methods exhibit a factor of 1.5 to 2.5 reduction in rms errors for areal accumulations over a 15 km2 NASA dense network housing 16 sites having collocated bucket gauges, with the higher error reductions best associated with polarimetric methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models

Clouds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have had difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors which are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept of instrument “simulators”, which convert model variables into pseudo-instrument observations, has evolved with the goal to improve and to facilitate the comparison of modeled clouds with observations. Many simulators have (and continue to be developed) for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the global climate modeling community to exploit satellite observations for model cloud evaluation (e.g., Klein et al. 2013; Zhang et al. 2010). This article introduces a ground-based cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to CloudSat radar observations, ARM radar measurements occur with higher temporal resolution and finer vertical resolution. This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are difficult for the CloudSat radar to detect due to surface contamination (Mace et al. 2007; Marchand et al. 2008). Therefore, the ARM ground-based cloud observations can provide important observations of clouds that complement measurements from space.

Zhang, Yuying↗
DOE OSTI2012

Scanning ARM Cloud Radar Handbook

The scanning ARM cloud radar (SACR) is a polarimetric Doppler radar consisting of three different radar designs based on operating frequency. These are designated as follows: (1) X-band SACR (X-SACR); (2) Ka-band SACR (Ka-SACR); and (3) W-band SACR (W-SACR). There are two SACRs on a single pedestal at each site where SACRs are deployed. The selection of the operating frequencies at each deployed site is predominantly determined by atmospheric attenuation at the site. Because RF attenuation increases with atmospheric water vapor content, ARM's Tropical Western Pacific (TWP) sites use the X-/Ka-band frequency pair. The Southern Great Plains (SGP) and North Slope of Alaska (NSA) sites field the Ka-/W-band frequency pair. One ARM Mobile Facility (AMF1) has a Ka/W-SACR and the other (AMF2) has a X/Ka-SACR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Ka-Band ARM Zenith Radar (KAZR) Instrument Handbook

The Ka-band ARM zenith radar (KAZR) is a zenith-pointing Doppler cloud radar operating at approximately 35 GHz. The KAZR is an evolutionary follow-on radar to ARM's widely successful millimeter-wavelength cloud radar (MMCR). The main purpose of the KAZR is to provide vertical profiles of clouds by measuring the first three Doppler moments: reflectivity, radial Doppler velocity, and spectra width. At the sites where the dual-polarization measurements are made, the Doppler moments for the cross-polarization channel are also available. In addition to the moments, velocity spectra are also continuously recorded for each range gate.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Ka-Band Scanning ARM Cloud Radar (a0)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Ka-Band Scanning ARM Cloud Radar (a1)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗